Data Scientist - I

Overview

• Data Analysis & Insights: Analyze large datasets from multiple sources (clickstream, sales, user engagement) to uncover insights and support business decision-making.
• Machine Learning: Develop, deploy, and maintain machine learning models for recommendations, personalization, customer segmentation, demand forecasting, and pricing.
• A/B Testing: Design and analyze A/B tests to evaluate the performance of product features, marketing campaigns, and user experiences.
• Data Pipeline Development: Work closely with data engineering teams to ensure the availability of accurate and timely data for analysis.
• Collaborate with Cross-functional Teams: Partner with product, marketing, and engineering teams to deliver actionable insights and improve platform performance.
• Data Visualization: Build dashboards and visualizations to communicate findings to stakeholders in an understandable and impactful way.
• Exploratory Analysis: Identify trends, patterns, and outliers to help guide business strategy and performance improvements.
  
  

• Proficiency in Python or R: Experience in using Python or R for data analysis and machine learning.
• SQL: Strong SQL skills to query and manipulate large datasets.
• Machine Learning Frameworks: Familiarity with libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Data Wrangling: Ability to clean, organize, and manipulate data from various sources.
• A/B Testing: Experience designing experiments and interpreting test results.
• Visualization Tools: Proficiency with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
• Statistics & Probability: Strong grasp of statistical methods, hypothesis testing, and probability theory.
• Communication: Ability to convey complex findings in clear, simple terms for non-technical stakeholders.
  
  

• E-commerce Experience: Experience working with e-commerce datasets (e.g., user behavior, transaction data, inventory, and product data).
• Experience with Big Data: Familiarity with big data technologies (e.g., Hadoop, Spark, BigQuery).
• Cloud Platforms: Experience with cloud platforms like AWS, GCP, or Azure for data storage and model deployment.
• Business Acumen: Understanding of key business metrics in e-commerce, such as conversion rates, LTV, and customer acquisition cost (CAC).
  
  

• Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related field.
  
  

Founded in 2011, Purplle has emerged as one of India’s premier omnichannel beauty destinations, redefining the way millions shop for beauty. With 1,000+ brands, 60,000+ products, and over 7 million monthly active users, Purplle has built a powerhouse platform that seamlessly blends online and offline experiences.

Expanding its footprint in 2022, Purplle introduced 6,000+ offline touchpoints and launched 100+ stores, strengthening its presence beyond digital. Beyond hosting third-party brands, Purplle has successfully scaled its own D2C powerhouses—FACES CANADA, Good Vibes, Carmesi, Purplle, and NY Bae—offering trend-driven, high-quality beauty essentials.

What sets Purplle apart is its technology driven hyper-personalized shopping experience. By curating detailed user personas, enabling virtual makeup trials, and delivering tailored product recommendations based on personality, search intent, and purchase behavior, Purplle ensures a unique, customer-first approach.

In 2022, Purplle achieved unicorn status, becoming India’s 102nd unicorn, backed by an esteemed group of investors including ADIA, Kedaara, Premji Invest, Sequoia Capital India, JSW Ventures, Goldman Sachs, Verlinvest, Blume Ventures, and Paramark Ventures.

With a 3,000+ strong team and an unstoppable vision, Purplle is set to lead the charge in India’s booming beauty landscape, revolutionizing the way the nation experiences beauty.